Automated classification of white matter lesions in multiple sclerosis patients' MRI images using gray level enhancement and deep learning

Mahsa Kargar Nigjeh; Hanieh Ajami; Scott E. Umbaugh

doi:10.1117/12.2688269

4 October 2023 Automated classification of white matter lesions in multiple sclerosis patients' MRI images using gray level enhancement and deep learning

Mahsa Kargar Nigjeh, Hanieh Ajami, Scott E. Umbaugh

Author Affiliations +

Proceedings Volume 12674, Applications of Digital Image Processing XLVI; 1267411 (2023) https://doi.org/10.1117/12.2688269
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

This paper presents an automated system for the classification of white matter lesions (WMLs) on the brain surface of multiple sclerosis (MS) patients' MRI images. The proposed method utilizes deep learning techniques, specifically the ResNet18 architecture, and incorporates gray level linear modification methods for image preprocessing. The objective is to accurately classify WMLs using a deep learning model, thereby reducing the workload of radiologists, and improving the accuracy of MS diagnosis. The study demonstrates the potential of using deep learning models to automate the detection and analysis of WMLs in MS patients' MRI images. By applying gray level linear modification methods, the visibility of lesion pixels is enhanced, facilitating their recognition. The ResNet18 architecture, trained using transfer learning, achieves an accuracy of up to 93% in classifying MRI images into two categories: those with and without WMLs. The results indicate that the proposed system offers an efficient tool for radiologists, enabling them to streamline the classification of WMLs and enhance the accuracy of MS diagnosis. The incorporation of gray level enhancement techniques provides improved visualization of lesion pixels, aiding in their identification and analysis.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Mahsa Kargar Nigjeh, Hanieh Ajami, and Scott E. Umbaugh "Automated classification of white matter lesions in multiple sclerosis patients' MRI images using gray level enhancement and deep learning", Proc. SPIE 12674, Applications of Digital Image Processing XLVI, 1267411 (4 October 2023); https://doi.org/10.1117/12.2688269

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KEYWORDS

Multiple sclerosis

Magnetic resonance imaging

Education and training

Deep learning

Image classification

White matter

RGB color model

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